Refactored Apriori implementation with correct pruning and candidate …

machine_learning/apriori_algorithm.py

@@ -1,5 +1,5 @@
 """
-Apriori Algorithm is a Association rule mining technique, also known as market basket
+Apriori Algorithm is an Association rule mining technique, also known as market basket
 analysis, aims to discover interesting relationships or associations among a set of
 items in a transactional or relational database.
 
@@ -11,7 +11,8 @@
 Examples: https://www.kaggle.com/code/earthian/apriori-association-rules-mining
 """
 
 from itertools import combinations
+from collections import defaultdict
 
 
 def load_data() -> list[list[str]]:
@@ -24,36 +25,30 @@
     return [["milk"], ["milk", "butter"], ["milk", "bread"], ["milk", "bread", "chips"]]
 
 
-def prune(itemset: list, candidates: list, length: int) -> list:
+def prune(
+    frequent_itemsets: list[list[str]], candidates: list[list[str]]
+) -> list[list[str]]:
     """
-    Prune candidate itemsets that are not frequent.
-    The goal of pruning is to filter out candidate itemsets that are not frequent.  This
-    is done by checking if all the (k-1) subsets of a candidate itemset are present in
-    the frequent itemsets of the previous iteration (valid subsequences of the frequent
-    itemsets from the previous iteration).
-
-    Prunes candidate itemsets that are not frequent.
-
-    >>> itemset = ['X', 'Y', 'Z']
-    >>> candidates = [['X', 'Y'], ['X', 'Z'], ['Y', 'Z']]
-    >>> prune(itemset, candidates, 2)
-    [['X', 'Y'], ['X', 'Z'], ['Y', 'Z']]
-
-    >>> itemset = ['1', '2', '3', '4']
-    >>> candidates = ['1', '2', '4']
-    >>> prune(itemset, candidates, 3)
-    []
+    Prunes candidate itemsets by ensuring all (k-1)-subsets exist in previous frequent itemsets.
+
+    >>> frequent_itemsets = [['X', 'Y'], ['X', 'Z'], ['Y', 'Z']]
+    >>> candidates = [['X', 'Y', 'Z'], ['X', 'Y', 'W']]
+    >>> prune(frequent_itemsets, candidates)
+    [['X', 'Y', 'Z']]
     """
-    pruned = []
+
+    previous_frequents = set(frozenset(itemset) for itemset in frequent_itemsets)
+
+    pruned_candidates = []
     for candidate in candidates:
-        is_subsequence = True
-        for item in candidate:
-            if item not in itemset or itemset.count(item) < length - 1:
-                is_subsequence = False
-                break
-        if is_subsequence:
-            pruned.append(candidate)
-    return pruned
+        all_subsets_frequent = all(
+            frozenset(subset) in previous_frequents
+            for subset in combinations(candidate, len(candidate) - 1)
+        )
+        if all_subsets_frequent:
+            pruned_candidates.append(candidate)
+
+    return pruned_candidates
 
 
 def apriori(data: list[list[str]], min_support: int) -> list[tuple[list[str], int]]:
@@ -62,52 +57,79 @@
 
     >>> data = [['A', 'B', 'C'], ['A', 'B'], ['A', 'C'], ['A', 'D'], ['B', 'C']]
     >>> apriori(data, 2)
-    [(['A', 'B'], 1), (['A', 'C'], 2), (['B', 'C'], 2)]
+    [(['A'], 4), (['B'], 3), (['C'], 3), (['A', 'B'], 2), (['A', 'C'], 2), (['B', 'C'], 2)]
 
     >>> data = [['1', '2', '3'], ['1', '2'], ['1', '3'], ['1', '4'], ['2', '3']]
     >>> apriori(data, 3)
-    []
+    [(['1'], 4), (['2'], 3), (['3'], 3)]
     """
-    itemset = [list(transaction) for transaction in data]
-    frequent_itemsets = []
-    length = 1
 
-    while itemset:
-        # Count itemset support
-        counts = [0] * len(itemset)
-        for transaction in data:
-            for j, candidate in enumerate(itemset):
-                if all(item in transaction for item in candidate):
-                    counts[j] += 1
+    item_counts = defaultdict(int)
+    for transaction in data:
+        for item in transaction:
+            item_counts[item] += 1
+
+    current_frequents = [
+        [item] for item, count in item_counts.items() if count >= min_support
+    ]
+    frequent_itemsets = [
+        ([item], count) for item, count in item_counts.items() if count >= min_support
+    ]
 
-        # Prune infrequent itemsets
-        itemset = [item for i, item in enumerate(itemset) if counts[i] >= min_support]
+    k = 2
+    while current_frequents:
+        candidates = [
+            sorted(list(set(i) | set(j)))
+            for i in current_frequents
+            for j in current_frequents
+            if len(set(i).union(j)) == k
+        ]
 
-        # Append frequent itemsets (as a list to maintain order)
-        for i, item in enumerate(itemset):
-            frequent_itemsets.append((sorted(item), counts[i]))
+        candidates = [list(c) for c in {frozenset(c) for c in candidates}]
 
-        length += 1
-        itemset = prune(itemset, list(combinations(itemset, length)), length)
+        candidates = prune(current_frequents, candidates)
 
-    return frequent_itemsets
+        candidate_counts = defaultdict(int)
+        for transaction in data:
+            t_set = set(transaction)
+            for candidate in candidates:
+                if set(candidate).issubset(t_set):
+                    candidate_counts[tuple(sorted(candidate))] += 1
+
+        current_frequents = [
+            list(key) for key, count in candidate_counts.items() if count >= min_support
+        ]
+        frequent_itemsets.extend(
+            [
+                (list(key), count)
+                for key, count in candidate_counts.items()
+                if count >= min_support
+            ]
+        )
+
+        k += 1
+
+    return sorted(frequent_itemsets, key=lambda x: (len(x[0]), x[0]))
 
 
 if __name__ == "__main__":
     """
     Apriori algorithm for finding frequent itemsets.
 
-    Args:
-        data: A list of transactions, where each transaction is a list of items.
-        min_support: The minimum support threshold for frequent itemsets.
+    This script loads sample transaction data and runs the Apriori algorithm
+    with a user-defined minimum support threshold.
 
-    Returns:
-        A list of frequent itemsets along with their support counts.
+    The result is a list of frequent itemsets along with their support counts.
     """
     import doctest
 
     doctest.testmod()
 
-    # user-defined threshold or minimum support level
-    frequent_itemsets = apriori(data=load_data(), min_support=2)
-    print("\n".join(f"{itemset}: {support}" for itemset, support in frequent_itemsets))
+    transactions = load_data()
+    min_support_threshold = 2
+
+    frequent_itemsets = apriori(transactions, min_support=min_support_threshold)
+
+    print("Frequent Itemsets:")
+    for itemset, support in frequent_itemsets:
+        print(f"{itemset}: {support}")